DBPedia Classes

DBpedia is a knowledge graph extracted from Wikipedia, providing structured data about real-world entities and their relationships. DBpedia Classes are the core building blocks of this knowledge graph, representing different categories or types of entities.

Key Concepts:

Entity: A real-world object, such as a person, place, thing, or concept. Class: A group of entities that share common properties or characteristics. Instance: A specific member of a class.

Examples of DBPedia Classes:

Person: Represents individuals, e.g., "Barack Obama," "Albert Einstein." Place: Represents locations, e.g., "Paris," "Mount Everest." Organization: Represents groups, institutions, or companies, e.g., "Google," "United Nations." Event: Represents occurrences, e.g., "World Cup," "French Revolution." Artwork: Represents creative works, e.g., "Mona Lisa," "Star Wars."

Hierarchy and Relationships:

DBpedia classes often have a hierarchical structure, where subclasses inherit properties from their parent classes. For example, the class "Person" might have subclasses like "Politician," "Scientist," and "Artist."

Relationships between classes are also important. For instance, a "Person" might have a "birthPlace" relationship with a "Place," or an "Artist" might have a "hasArtwork" relationship with an "Artwork."

Applications of DBPedia Classes:

Semantic Search: DBPedia classes can be used to enhance search results by understanding the context and meaning of queries.

Knowledge Graph Construction: DBPedia classes form the foundation of knowledge graphs, which can be used for various applications like question answering, recommendation systems, and data integration.

Data Analysis: DBPedia classes can be used to analyze and extract insights from large datasets.

The dataset used in the paper is a real-world graph dataset, where the goal is to recover signal values on test nodes.

Description: this corpus was designed as an experimental benchmark for a task of signed graph classification. It is composed of three datasets derived from external sources and adapted to our needs:

SpaceOrigin Conversations [1]: set of conversational graphs, each one associated to a situation of verbal abuse vs. normal situation. These conversations model interactions happening in chatrooms hosted by an MMORPG/ The graphs were originally unsigned: we attributed signed to the edges based on the polarity of the exchanged messages.

Correlation Clustering Instances [2]: set of graph generated randomly as instances of the Correlation Clustering problem, which consists in partitioning signed graphs. These graphs are not associated in any class in the original paper. We proposed a class based on certain features of the space of optimal solutions explored in [2].

European Parliament Roll-Calls [3]: vote networks extracted from the activity of French Members of the European Parliament. The original data does not have any class associated to the networks: we proposed one based on the number of political factions identified in each network in [3].

These data were used in [4] in order to train and assess various representation learning methods. The authors proposed Signed Graph2vec, a signed variant of Graph2vec; WSGCN, a whole-graph variant of Signed Graph Convolutional Networks (SGCN), and use an aggregated version of Signed Network Embeddings (SiNE) as a baseline. The article provides more information regarding the properties of the datasets, and how they were constituted.

Software: the software used to train the representation learning methods and classifiers is publicly available online: SWGE.

References:

Papegnies, É.; Labatut, V.; Dufour, R. & Linarès, G. Conversational Networks for Automatic Online Moderation. IEEE Transactions on Computational Social Systems, 2019, 6:38-55. DOI: 10.1109/TCSS.2018.2887240 ⟨hal-01999546⟩

Arınık, N.; Figueiredo, R. & Labatut, V. Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs. Journal of Complex Networks, 2020, 8(6):cnaa025. DOI: 10.1093/comnet/cnaa025 ⟨hal-02994011⟩

Arınık, N.; Figueiredo, R. & Labatut, V. Multiple partitioning of multiplex signed networks: Application to European parliament votes. Social Networks, 2020, 60:83-102. DOI: 10.1016/j.socnet.2019.02.001 ⟨hal-02082574⟩

Cécillon, N.; Labatut, V.; Dufour, R. & Arınık, N. Whole-Graph Representation Learning For the Classification of Signed Networks. IEEE Access, 2024, 12:151303-151316. DOI: 10.1109/ACCESS.2024.3472474 ⟨hal-04712854⟩

Funding: part of this work was funded by a grant from the Provence-Alpes-Côte-d'Azur region (PACA, France) and the Nectar de Code company.

Citation: If you use this data or the associated source code, please cite article [4]:

@Article{Cecillon2024, author = {Cécillon, Noé and Labatut, Vincent and Dufour, Richard and Arınık, Nejat}, title = {Whole-Graph Representation Learning For the Classification of Signed Networks}, journal = {IEEE Access}, year = {2024}, volume = {12}, pages = {151303-151316}, doi = {10.1109/ACCESS.2024.3472474},}

Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

The global graph database market size was USD 1.1 billion in 2024 & is projected to grow from USD 1.42 billion in 2025 to USD 17.88 billion by 2033.
Report Scope:

Snapshot img

Graph Database Market Size 2025-2029

The graph database market size is valued to increase by USD 11.24 billion, at a CAGR of 29% from 2024 to 2029. Open knowledge network gaining popularity will drive the graph database market.

Market Insights

North America dominated the market and accounted for a 46% growth during the 2025-2029.
By End-user - Large enterprises segment was valued at USD 1.51 billion in 2023
By Type - RDF segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 670.01 million 
Market Future Opportunities 2024: USD 11235.10 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth due to the increasing demand for low-latency query capabilities and the ability to handle complex, interconnected data. Graph databases are deployed in both on-premises data centers and cloud regions, providing flexibility for businesses with varying IT infrastructures. One real-world business scenario where graph databases excel is in supply chain optimization. In this context, graph databases can help identify the shortest path between suppliers and consumers, taking into account various factors such as inventory levels, transportation routes, and demand patterns. This can lead to increased operational efficiency and reduced costs.
However, the market faces challenges such as the lack of standardization and programming flexibility. Graph databases, while powerful, require specialized skills to implement and manage effectively. Additionally, the market is still evolving, with new players and technologies emerging regularly. Despite these challenges, the potential benefits of graph databases make them an attractive option for businesses seeking to gain a competitive edge through improved data management and analysis.

What will be the size of the Graph Database Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market is an evolving landscape, with businesses increasingly recognizing the value of graph technology for managing complex and interconnected data. According to recent research, the adoption of graph databases is projected to grow by over 20% annually, surpassing traditional relational databases in certain use cases. This trend is particularly significant for industries requiring advanced data analysis, such as finance, healthcare, and telecommunications. Compliance is a key decision area where graph databases offer a competitive edge. By modeling data as nodes and relationships, organizations can easily trace and analyze interconnected data, ensuring regulatory requirements are met. Moreover, graph databases enable real-time insights, which is crucial for budgeting and product strategy in today's fast-paced business environment.
Graph databases also provide superior performance compared to traditional databases, especially in handling complex queries involving relationships and connections. This translates to significant time and cost savings, making it an attractive option for businesses seeking to optimize their data management infrastructure. In conclusion, the market is experiencing robust growth, driven by its ability to handle complex data relationships and offer real-time insights. This trend is particularly relevant for industries dealing with regulatory compliance and seeking to optimize their data management infrastructure.

Unpacking the Graph Database Market Landscape

In today's data-driven business landscape, the adoption of graph databases has surged due to their unique capabilities in handling complex network data modeling. Compared to traditional relational databases, graph databases offer a significant improvement in query performance for intricate relationship queries, with some reports suggesting up to a 500% increase in query response time. Furthermore, graph databases enable efficient data lineage tracking, ensuring regulatory compliance and enhancing data version control. Graph databases, such as property graph models and RDF databases, facilitate node relationship management and real-time graph processing, making them indispensable for industries like finance, healthcare, and social media. With the rise of distributed and knowledge graph databases, organizations can achieve scalability and performance improvements, handling massive datasets with ease. Security, indexing, and deployment are essential aspects of graph databases, ensuring data integrity and availability. Query performance tuning and graph analytics libraries further enhance the value of graph databases in data integration and business intelligence applications. Ultimately, graph databases offer a powerful alternative to NoSQL databases, providing a more flexible and efficient approach to managing complex data relationships.

Key Market Drivers Fueling Growth

The growing popularity o

GraphLand benchmark is introduced in the paper GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data. It provides node property prediction datasets from real-world industrial applications of graph machine learning.

• Multiclass node classification:
  • hm-categories
  • pokec-regions
  • web-topics
• Binary node classification:
  • tolokers-2
  • city-reviews
  • artnet-exp
  • web-fraud
• Node regression:
  • hm-prices
  • avazu-ctr
  • city-roads-M
  • city-roads-L
  • twitch-views
  • artnet-views
  • web-traffic

Each dataset is provided in its own directory. Each dataset directory contains the following files: * edgelist.csv — graph edges in the edgelist format. Node that some datasets have directed graphs and some have undirected graphs (see info.yaml for each dataset). Regardless of this, the edges are always provided in a directed format used by graph deep learning libraries PyG and DGL, that is, if a graph is undirected, then each edge appears in the edgelist twice: as (u, v) and as (v, u). * targets.csv — node-level targets for the task, one per node. Contains NaNs if dataset has some unlabeled nodes. * features.csv — node-level features, one feature vector per node. Node features can be either numerical or categorical (see info.yaml for each dataset for lists of numerical and categorical features). Numerical features contain NaNs if some values are unknown. * split_masks_RL.csv — table with columns train, val, test containing masks for the RL (random low) split for the transductive setting (10%/10%/80% train/val/test random stratified split). * split_masks_RH.csv — table with columns train, val, test containing masks for the RH (random high) split for the transductive setting (50%/25%/25% train/val/test random stratified split). * split_masks_TH.csv — table with columns train, val, test containing masks for the TH (temporal high) split for the transductive and inductive settings (50%/25%/25% train/val/test temporal split). For the inductive setting, remove from the full graph all nodes and their incident edges from the val and test subsets to get the train graph, and remove from the full graph all nodes and their incident edges from the test subset to get the val graph. TH split is not provided for datasets which are almost static by nature (road networks) or for which there was no neccessary temporal information available: city-reviews, city-roads-M, city-roads-L, web-traffic. * info.yaml — a yaml dictionary with dataset metadata. Contains the following keys: * dataset_name — the name of the dataset. * task — prediction task, one of: multiclass classification, binary classification, regression. * metric — the recommended metric for evaluation. accuracy for multiclass classification, AP (average precision) for binary classification, R2 (R-squared, coefficient of determination) for regression. * graph_is_directed — a boolean value indicating whether the graph is directed. * has_unlabeled_nodes — a boolean value indicating if the dataset has unlabeled nodes. * has_nans_in_numerical_features — a boolean indicating if the dataset has NaNs in numerical features (categorical features never have NaNs as unknown values are simply encoded as a separate category). * target_name — the name of the target variable from the targets.csv file. * numerical_features_names — a list of names of all numerical features from features.csv. Numerical features can have widely different scales and distributions so in practice it might be useful to apply some transformation to them, e.g., standard scaling or a quantile transformation. * fraction_features_names — a subset of numerical_features_names, a list of names of all numerical features that have the meaning of fractions and are thus always in [0, 1] range. These features are specified because due to their range it may not be neccessary to apply transformations to them in contrast to other numerical features. * categorical_features_names — a list of names of all categorical features from features.csv. In practice it might be useful to apply one-hot encoding to them. Each feature from features.csv is either in numerical_features_names or in categorical_features_names.

GraphLand datasets are provided under the Apache 2.0 license.

If you found GraphLand datasets useful, please cite the following work:

@article{bazhenov2025graphland,
 title={{GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data}},
 author={Bazhenov, Gleb and Platonov, Oleg and Prokhorenkova, Liudmila},
 journal={arXiv preprint},
 year={2025}
}

Characteristics of datasets used in the experiments, where |V| is the number of nodes, |E| is the number of edges, |C| is the number of communities and sizes is the range of the number of nodes in each community.

GP Knowledge Graph is a dynamic, automatically updated map of the global economy that captures hierarchical relationships, correlations, and influential drivers across tens of thousands of socio-economic time series.

Graph and download economic data for World Uncertainty Index: Global: GDP Weighted Average (WUIGLOBALWEIGHTAVG) from Q1 1990 to Q3 2025 about uncertainty, average, World, GDP, and indexes.

Three temporal graph datasets for node classification under distribution shift.

DBLP-Easy and DBLP-Hard are citation graph datasets. PharmaBio is a collaboration graph dataset.

Vertices are scientific publications, edges are either citations (DBLP) or at-least-one-common-author relationships (PharmaBio).

The task is to classify the vertices of the graph into the respective conference/journal venues (DBLP) or journal categories (PharmaBio). In the DBLP datasets, new classes may appear over time.

Each dataset follows the structure:

• adjlist.txt -- the graph structure encoded as adjacency lists: in each row, the first entry is the source vertex, the remaining entries are adjacent vertices

• X.npy -- numpy serialized format for node features indexed by node id corresponding to adjlist.txt

• y.npy -- numpy serialized format for node labels indexed by node id corresponding to adjlist.txt

• t.npy -- numpy serialized format for time steps indexed by node id corresponding to adjlist.txt

A paper describing our incremental training and evaluation framework is published in IJCNN 2021 (Pre-print on arXiv: https://arxiv.org/abs/2006.14422).

If you use these datasets in your research, please cite the corresponding paper:

@inproceedings{galke2021lifelong, author={Galke, Lukas and Franke, Benedikt and Zielke, Tobias and Scherp, Ansgar}, booktitle={2021 International Joint Conference on Neural Networks (IJCNN)}, title={Lifelong Learning of Graph Neural Networks for Open-World Node Classification}, year={2021}, volume={}, number={}, pages={1-8}, doi={10.1109/IJCNN52387.2021.9533412} }

This database collates 3552 development indicators from different studies with data by country and year, including single year and multiple year time series. The data is presented as charts, the data can be downloaded from linked project pages/references for each set, and the data for each presented graph is available as a CSV file as well as a visual download of the graph (both available via the download link under each chart).

Graph and download economic data for Population Growth for Brazil (SPPOPGROWBRA) from 1961 to 2024 about Brazil, population, and rate.

The global graph database market size reached USD 2.0 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 8.6 Billion by 2033, exhibiting a growth rate (CAGR) of 17.57% during 2025-2033. The increasing adoption of graph databases in cybersecurity for threat detection and network analysis, growing demand for real-time analytics and AI-driven insights, and expanding application in industries, such as healthcare and finance, for data integration and personalized services, are some of the key factors catalyzing the market growth.

IMARC Group provides an analysis of the key trends in each segment of the global graph database market report, along with forecasts at the global, regional, and country levels from 2025-2033. Our report has categorized the market based on component, type of database, analysis type, deployment model, application, and industry vertical.

The global graph analytics market size was USD 104.09 million in 2024 & is projected to grow from USD 140.51 million in 2025 to USD 1550.22 million by 2033.
Report Scope:

Graph drawing, involving the automatic layout of graphs, is vital for clear data visualization and interpretation but poses challenges due to the optimization of a multi-metric objective function, an area where current search-based methods seek improvement. In this paper, we investigate the performance of Jaya algorithm for automatic graph layout with straight lines. Jaya algorithm has not been previously used in the field of graph drawing. Unlike most population-based methods, Jaya algorithm is a parameter-less algorithm in that it requires no algorithm-specific control parameters and only population size and number of iterations need to be specified, which makes it easy for researchers to apply in the field. To improve Jaya algorithm’s performance, we applied Latin Hypercube Sampling to initialize the population of individuals so that they widely cover the search space. We developed a visualization tool that simplifies the integration of search methods, allowing for easy performance testing of algorithms on graphs with weighted aesthetic metrics. We benchmarked the Jaya algorithm and its enhanced version against Hill Climbing and Simulated Annealing, commonly used graph-drawing search algorithms which have a limited number of parameters, to demonstrate Jaya algorithm’s effectiveness in the field. We conducted experiments on synthetic datasets with varying numbers of nodes and edges using the Erdős–Rényi model and real-world graph datasets and evaluated the quality of the generated layouts, and the performance of the methods based on number of function evaluations. We also conducted a scalability experiment on Jaya algorithm to evaluate its ability to handle large-scale graphs. Our results showed that Jaya algorithm significantly outperforms Hill Climbing and Simulated Annealing in terms of the quality of the generated graph layouts and the speed at which the layouts were produced. Using improved population sampling generated better layouts compared to the original Jaya algorithm using the same number of function evaluations. Moreover, Jaya algorithm was able to draw layouts for graphs with 500 nodes in a reasonable time.

Freebase is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong type system; its properties are purposefully represented in reverse pairs; and it uses mediator objects to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. We make available several variants of the Freebase dataset by inclusion and exclusion of these data modeling idiosyncrasies. This is the first-ever publicly available full-scale Freebase dataset that has gone through proper preparation.

Dataset Details

The dataset consists of the four variants of Freebase dataset as well as related mapping/support files. For each variant, we made three kinds of files available:

• Subject matter triples file
  • fb+/-CVT+/-REV One folder for each variant. In each folder there are 5 files: train.txt, valid.txt, test.txt, entity2id.txt, relation2id.txt Subject matter triples are the triples belong to subject matters domains—domains describing real-world facts.
    • Example of a row in train.txt, valid.txt, and test.txt:
      • 2, 192, 0
    • Example of a row in entity2id.txt:
      • /g/112yfy2xr, 2
    • Example of a row in relation2id.txt:
      • /music/album/release_type, 192
    • Explaination
      • "/g/112yfy2xr" and "/m/02lx2r" are the MID of the subject entity and object entity, respectively. "/music/album/release_type" is the realtionship between the two entities. 2, 192, and 0 are the IDs assigned by the authors to the objects.
• Type system file
  • freebase_endtypes: Each row maps an edge type to its required subject type and object type.
    • Example
      • 92, 47178872, 90
    • Explanation
      • "92" and "90" are the type id of the subject and object which has the relationship id "47178872".
• Metadata files
  • object_types: Each row maps the MID of a Freebase object to a type it belongs to.
    • Example
      • /g/11b41c22g, /type/object/type, /people/person
    • Explanation
      • The entity with MID "/g/11b41c22g" has a type "/people/person"
  • object_names: Each row maps the MID of a Freebase object to its textual label.
    • Example
      • /g/11b78qtr5m, /type/object/name, "Viroliano Tries Jazz"@en
    • Explanation
      • The entity with MID "/g/11b78qtr5m" has name "Viroliano Tries Jazz" in English.
  • object_ids: Each row maps the MID of a Freebase object to its user-friendly identifier.
    • Example
      • /m/05v3y9r, /type/object/id, "/music/live_album/concert"
    • Explanation
      • The entity with MID "/m/05v3y9r" can be interpreted by human as a music concert live album.
  • domains_id_label: Each row maps the MID of a Freebase domain to its label.
    • Example
      • /m/05v4pmy, geology, 77
    • Explanation
      • The object with MID "/m/05v4pmy" in Freebase is the domain "geology", and has id "77" in our dataset.
  • types_id_label: Each row maps the MID of a Freebase type to its label.
    • Example
      • /m/01xljxh, /government/political_party, 147
    • Explanation
      • The object with MID "/m/01xljxh" in Freebase is the type "/government/political_party", and has id "147" in our dataset.
  • entities_id_label: Each row maps the MID of a Freebase entity to its label.
    • Example
      • /g/11b78qtr5m, Viroliano Tries Jazz, 2234
    • Explanation
      • The entity with MID "/g/11b78qtr5m" in Freebase is "Viroliano Tries Jazz", and has id "2234" in our dataset.
    • properties_id_label: Each row maps the MID of a Freebase property to its label.
      • Example
        • /m/010h8tp2, /comedy/comedy_group/members, 47178867
      • Explanation
        • The object with MID "/m/010h8tp2" in Freebase is a property(relation/edge), it has label "/comedy/comedy_group/members" and has id "47178867" in our dataset.
    • uri_original2simplified and uri_simplified2original: The mapping between original URI and simplified URI and the mapping between simplified URI and original URI repectively.
      • Example
        • uri_original2simplified
          • "http://rdf.freebase.com/ns/type.property.unique": "/type/property/unique"
        • uri_simplified2original
          • "/type/property/unique": "http://rdf.freebase.com/ns/type.property.unique"
      • Explanation
        • The URI "http://rdf.freebase.com/ns/type.property.unique" in the original Freebase RDF dataset is simplified into "/type/property/unique" in our dataset.
        • The identifier "/type/property/unique" in our dataset has URI http://rdf.freebase.com/ns/type.property.unique in the original Freebase RDF dataset.

Global trade data of Graph under 73182990, 73182990 global trade data, trade data of Graph from 80+ Countries.

Global Graph Technology Market Share size and share are expected to exceed USD 23.48 billion by 2032, with a compound annual growth rate (CAGR) of 21.9% during the forecast period.

Graph and download economic data for Population Ages 0 to 14 for World (SPPOP0014TOZSWLD) from 1960 to 2024 about 0 to 14 years and population.